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Biostatistics. 2008 Jul;9(3):432-41. Epub 2007 Dec 12.

Sparse inverse covariance estimation with the graphical lasso.

Author information

1
Department of Statistics, Stanford University, CA 94305, USA.

Abstract

We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

PMID:
18079126
PMCID:
PMC3019769
DOI:
10.1093/biostatistics/kxm045
[Indexed for MEDLINE]
Free PMC Article

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